estimation of age in forensic medicine using multivariate data analysis ivan belyaev altai state...
TRANSCRIPT
Estimation of age in forensic medicine using multivariate data
analysis
Ivan Belyaev
Altai State University, Barnaul, Russia
Sergey Kucheryavsky
Aalborg University Esbjerg, ACABS group, Esbjerg, Denmark
WSC'6Kazan–2008
Outline
Introduction Approach which used for age and sex
identification in forensic medicine Description of proposed solution, using
multivariate data analysis Results and discussion
2
2
Introduction
Skeletons' bones are resistant to environment transformations in course of time
Osteoporosis is an essential factor for age-specific changes, characterized by decrease of bone tissue density
3
Osteoporosis marks
Sponge
Cortical
Bone structure
4
Age
Sp
on
ge
den
sity
22 years
56 years
83 years
Photocolorimeter scheme
PhotodetectorLight sourceSample
6
Image classification procedure
imageacquisition
imagepreprocessing features extracting classification
Digitalcamera,
Scaner
SegmentationWavelet transform,
AMTPLS
7
Image acquisition and preprocessing
Digital camera Scanner
8
Building features vector
Sponge part of bone has textural structure Therefore, the following was chosen:
Feature vector, based on wavelet transformation good results in different areas of image recognition and analysis quick and simple algorithm
Feature vector, based on AMT good result in classification of both heterogeneous images and
textures simple algorithm but relatively slow for big images (1-4 seconds
in comparison with Wavelet transformation –- 0.2-0.8 seconds)
9
Features vector: wavelet transformation
Raw signal
Smoothed Details
H G
Smoothed Details
H G
…
H – gives smoothed signalG – gives the details
1D signal
Gr
Hr
diag
hor
ver
HrHc HrGc GrHc GrGc
r — rows
c — columns
2D signal
10
Example of image WT
WT
11
Features vector: wavelet transformation
For feature vector we calculate metrics ofhorizontal, vertical and diagonal details:
Feature vector = [ f(dh1),f(dv
1), f(dd
1),…,f(dh
m),f(dv
m),f(dd
m) ]
1…m level of wavelet transformdh, dv, dd horizontal, vertical and diagonal detailsf() metrics function
Useful metrics: Energy Standard deviation Entropy
12
Experiments Samples:
Sponge tissue of I-V lumbar vertebra bodies without evident bone pathology
Samples were taken from 70 males and 62 females Aged from 21 to 93
Models:
Name Response
A-top AMT top age PLSA-bott AMT bottom age PLSA-both AMT both age PLSW-top WT top age PLSW-bott WT bottom age PLSW-both WT both age PLS
Features extraction
method
Lamina sides
Model constructing
method
13
Typical samples
Male,26 years
Male,40
years
Male,57
years
Male,71
years
A-b
oth
mod
el,
fem
ale
set
First resultsW
-bot
h m
odel
, fe
mal
e se
t
The basic reason of poor results
In our experiments the response variable is chronological age
Pores transformations reflect genuine grade of organism ageing, which in medicine is described as biological age
Biological age ≠ Chronological age
16
Correlation0.990.98
Correlation0.990.98
W-b
oth
mod
el,
fem
ale
set
A-b
oth
mod
el,
fem
ale
set
17
A-b
oth
A-t
op
A-b
ott
W-b
oth
W-t
op
W-b
ott0%
25%
50%
75%
100%
Less than 1 from 1 to 2 from 2 to 3
From 3 to 4 More than 5
A-b
oth
A-t
op
A-b
ott
W-b
oth
W-t
op
W-b
ott
Ph
0%
25%
50%
75%
100%
less than 5 from 5 to10 from 10 to 20 more than 20
Statistics for male samples prediction
Calibration set Full set
18
Statistics for female samples prediction
Full setCalibration set
A-b
oth
A-t
op
A-b
ott
W-b
oth
W-t
op
W-b
ott
Ph
0%
25%
50%
75%
100%
less than 5 from 5 to10 from 10 to 20 more than 20
A-b
oth
A-t
op
A-b
ott
W-b
oth
W-t
op
W-b
ott0%
25%
50%
75%
100%
Less than 1 from 1 to 2 from 2 to 3
From 3 to 4 More than 519
Problems and possible solutions
Problems
Not enough samples – can’t use test validation
Poor representativenes
Possible solution
use additional features
use other methods of analysis
20